critic权重法python代码

critic权重法是一种常用的强化学习方法，用于在训练过程中评估和更新策略网络。以下是一个简单的Python实现示例，使用TensorFlow库： ```python import tensorflow as tf import numpy as np class Critic: def __init__(self, state_dim, action_dim): self.state_dim = state_dim self.action_dim = action_dim self.model = self.build_model() def build_model(self): state_input = tf.keras.layers.Input(shape=(self.state_dim,)) action_input = tf.keras.layers.Input(shape=(self.action_dim,)) concat = tf.keras.layers.Concatenate()([state_input, action_input]) dense1 = tf.keras.layers.Dense(64, activation='relu')(concat) dense2 = tf.keras.layers.Dense(64, activation='relu')(dense1) output = tf.keras.layers.Dense(1, activation='linear')(dense2) model = tf.keras.models.Model(inputs=[state_input, action_input], outputs=output) model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss='mse') return model def train(self, states, actions, targets, epochs=1): self.model.fit([states, actions], targets, epochs=epochs, verbose=0) def predict(self, state, action): return self.model.predict([np.array([state]), np.array([action])])[0] class Actor: def __init__(self, state_dim, action_dim): self.state_dim = state_dim self.action_dim = action_dim self.model = self.build_model() def build_model(self): state_input = tf.keras.layers.Input(shape=(self.state_dim,)) dense1 = tf.keras.layers.Dense(64, activation='relu')(state_input) dense2 = tf.keras.layers.Dense(64, activation='relu')(dense1) output = tf.keras.layers.Dense(self.action_dim, activation='tanh')(dense2) model = tf.keras.models.Model(inputs=state_input, outputs=output) model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001)) return model def act(self, state): return self.model.predict(np.array([state]))[0] def update_critic(actor, critic, state, action, reward, next_state, done, gamma=0.99): target = reward + (1 - done) * gamma * critic.predict(next_state, actor.act(next_state)) critic.train(state, action, target) def main(): state_dim = 4 action_dim = 2 actor = Actor(state_dim, action_dim) critic = Critic(state_dim, action_dim) state = np.random.rand(state_dim) action = actor.act(state) reward = np.random.rand() next_state = np.random.rand(state_dim) done = False update_critic(actor, critic, state, action, reward, next_state, done) if __name__ == "__main__": main() ``` 这个示例展示了如何使用TensorFlow库实现一个简单的critic权重法。代码中包含了一个Critic类和Actor类，分别用于评估策略和生成动作。update_critic函数用于更新critic网络的权重。